IBM Watson Studio on Cloud Pak for Data vs. NVIDIA RAPIDS

It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.

Incentivized

NVIDIA RAPIDS drastically improves our productivity with near-interactive data science. And increases machine learning model accuracy by iterating on models faster and deploying them more frequently. It gives us the freedom to execute end-to-end data science and analytics pipelines.

Incentivized

• Integration of IBM Watson APIs such as speech to text, image recognition, personality insights, etc.
• SPSS modeler and neural network model provide no-code environments for data scientists to build pipelines quickly.
• Enforced best-practices set up POCs for deployment in production with a minimum of re-work.
• Estimator validation lets data scientists test and prove different models.

Incentivized

• Visualization
• Deep learning pipeline
• State of the art libraries

Incentivized

• The cost is steep and so only companies with resources can afford it
• It will be nice to have Chinese versions so that Chinese engineers can also use it easily
• It takes a while to learn how to input different kinds of skin defects for detection

Incentivized

• Its not flexible and cost effective for all sizes of organizations.
• I appreciate it has hassle-free integration.

Incentivized

because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)

Incentivized

The UI flawlessly merges this offering by providing a neat, minimal, responsive interface

Incentivized

From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected

Incentivized

Never had slow response even on our very busy network

Incentivized

I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path

Incentivized

The trainers on the job are very smart with solutions and very able in teaching

Incentivized

The Platform is very handy and suggests further steps according my previous interests

Incentivized

It surprised us with unpredictable case of use and brand new points of view

Incentivized

The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.

Incentivized

RAPIDS GPU accelerates machine learning to make the entire data science and analytics workflows run faster, also helps build databases and machine learning applications effectively. It also allows faster model deployment and iterations to increase machine learning model accuracy. The great value of money.

Incentivized

It helped us in getting from 0 to DSX without getting lost

Incentivized

• Could instantly show data driven insights to drive 20% incremental revenue over existing results
• Still don't have a real use case for unstructured data like twitter feed
• Some of the insights around user actions have driven new projects to automate mundane tasks

Incentivized

• Efficient way to complete tasks
• De-facto GPUs standard

Incentivized